Cause Of Death¶

Problem Statement:¶

This contains the problem statement and business goal¶

Content¶

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly.offline import init_notebook_mode
init_notebook_mode(connected=True)

import warnings
warnings.filterwarnings('ignore')
In [3]:
df=pd.read_csv(r"C:\Users\Kundan Patil\DS0522\INTERNSHIP\project\cause_of_deaths dataset.csv")
In [4]:
df
Out[4]:
Country/Territory Code Year Meningitis Alzheimer's Disease and Other Dementias Parkinson's Disease Nutritional Deficiencies Malaria Drowning Interpersonal Violence ... Diabetes Mellitus Chronic Kidney Disease Poisonings Protein-Energy Malnutrition Road Injuries Chronic Respiratory Diseases Cirrhosis and Other Chronic Liver Diseases Digestive Diseases Fire, Heat, and Hot Substances Acute Hepatitis
0 Afghanistan AFG 1990 2159 1116 371 2087 93 1370 1538 ... 2108 3709 338 2054 4154 5945 2673 5005 323 2985
1 Afghanistan AFG 1991 2218 1136 374 2153 189 1391 2001 ... 2120 3724 351 2119 4472 6050 2728 5120 332 3092
2 Afghanistan AFG 1992 2475 1162 378 2441 239 1514 2299 ... 2153 3776 386 2404 5106 6223 2830 5335 360 3325
3 Afghanistan AFG 1993 2812 1187 384 2837 108 1687 2589 ... 2195 3862 425 2797 5681 6445 2943 5568 396 3601
4 Afghanistan AFG 1994 3027 1211 391 3081 211 1809 2849 ... 2231 3932 451 3038 6001 6664 3027 5739 420 3816
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6115 Zimbabwe ZWE 2015 1439 754 215 3019 2518 770 1302 ... 3176 2108 381 2990 2373 2751 1956 4202 632 146
6116 Zimbabwe ZWE 2016 1457 767 219 3056 2050 801 1342 ... 3259 2160 393 3027 2436 2788 1962 4264 648 146
6117 Zimbabwe ZWE 2017 1460 781 223 2990 2116 818 1363 ... 3313 2196 398 2962 2473 2818 2007 4342 654 144
6118 Zimbabwe ZWE 2018 1450 795 227 2918 2088 825 1396 ... 3381 2240 400 2890 2509 2849 2030 4377 657 139
6119 Zimbabwe ZWE 2019 1450 812 232 2884 2068 827 1434 ... 3460 2292 405 2855 2554 2891 2065 4437 662 136

6120 rows × 34 columns

In [5]:
df.shape
Out[5]:
(6120, 34)
In [6]:
df.dtypes
Out[6]:
Country/Territory                             object
Code                                          object
Year                                           int64
Meningitis                                     int64
Alzheimer's Disease and Other Dementias        int64
Parkinson's Disease                            int64
Nutritional Deficiencies                       int64
Malaria                                        int64
Drowning                                       int64
Interpersonal Violence                         int64
Maternal Disorders                             int64
HIV/AIDS                                       int64
Drug Use Disorders                             int64
Tuberculosis                                   int64
Cardiovascular Diseases                        int64
Lower Respiratory Infections                   int64
Neonatal Disorders                             int64
Alcohol Use Disorders                          int64
Self-harm                                      int64
Exposure to Forces of Nature                   int64
Diarrheal Diseases                             int64
Environmental Heat and Cold Exposure           int64
Neoplasms                                      int64
Conflict and Terrorism                         int64
Diabetes Mellitus                              int64
Chronic Kidney Disease                         int64
Poisonings                                     int64
Protein-Energy Malnutrition                    int64
Road Injuries                                  int64
Chronic Respiratory Diseases                   int64
Cirrhosis and Other Chronic Liver Diseases     int64
Digestive Diseases                             int64
Fire, Heat, and Hot Substances                 int64
Acute Hepatitis                                int64
dtype: object
In [7]:
df.describe()
Out[7]:
Year Meningitis Alzheimer's Disease and Other Dementias Parkinson's Disease Nutritional Deficiencies Malaria Drowning Interpersonal Violence Maternal Disorders HIV/AIDS ... Diabetes Mellitus Chronic Kidney Disease Poisonings Protein-Energy Malnutrition Road Injuries Chronic Respiratory Diseases Cirrhosis and Other Chronic Liver Diseases Digestive Diseases Fire, Heat, and Hot Substances Acute Hepatitis
count 6120.000000 6120.000000 6120.000000 6120.000000 6120.000000 6120.000000 6120.000000 6120.000000 6120.000000 6120.000000 ... 6120.000000 6120.000000 6120.000000 6120.000000 6120.000000 6.120000e+03 6120.000000 6120.000000 6120.000000 6120.000000
mean 2004.500000 1719.701307 4864.189379 1173.169118 2253.600000 4140.960131 1683.333170 2083.797222 1262.589216 5941.898529 ... 5138.704575 4724.132680 425.013399 1965.994281 5930.795588 1.709237e+04 6124.072059 10725.267157 588.711438 618.429902
std 8.656149 6672.006930 18220.659072 4616.156238 10483.633601 18427.753137 8877.018366 6917.006075 6057.973183 21011.962487 ... 16773.081040 16470.429969 2022.640521 8255.999063 24097.784291 1.051572e+05 20688.118580 37228.051096 2128.595120 4186.023497
min 1990.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000e+00 0.000000 0.000000 0.000000 0.000000
25% 1997.000000 15.000000 90.000000 27.000000 9.000000 0.000000 34.000000 40.000000 5.000000 11.000000 ... 236.000000 145.750000 6.000000 5.000000 174.750000 2.890000e+02 154.000000 284.000000 17.000000 2.000000
50% 2004.500000 109.000000 666.500000 164.000000 119.000000 0.000000 177.000000 265.000000 54.000000 136.000000 ... 1087.000000 822.000000 52.500000 92.000000 966.500000 1.689000e+03 1210.000000 2185.000000 126.000000 15.000000
75% 2012.000000 847.250000 2456.250000 609.250000 1167.250000 393.000000 698.000000 877.000000 734.000000 1879.000000 ... 2954.000000 2922.500000 254.000000 1042.500000 3435.250000 5.249750e+03 3547.250000 6080.000000 450.000000 160.000000
max 2019.000000 98358.000000 320715.000000 76990.000000 268223.000000 280604.000000 153773.000000 69640.000000 107929.000000 305491.000000 ... 273089.000000 222922.000000 30883.000000 202241.000000 329237.000000 1.366039e+06 270037.000000 464914.000000 25876.000000 64305.000000

8 rows × 32 columns

In [8]:
df.isnull().sum()
Out[8]:
Country/Territory                             0
Code                                          0
Year                                          0
Meningitis                                    0
Alzheimer's Disease and Other Dementias       0
Parkinson's Disease                           0
Nutritional Deficiencies                      0
Malaria                                       0
Drowning                                      0
Interpersonal Violence                        0
Maternal Disorders                            0
HIV/AIDS                                      0
Drug Use Disorders                            0
Tuberculosis                                  0
Cardiovascular Diseases                       0
Lower Respiratory Infections                  0
Neonatal Disorders                            0
Alcohol Use Disorders                         0
Self-harm                                     0
Exposure to Forces of Nature                  0
Diarrheal Diseases                            0
Environmental Heat and Cold Exposure          0
Neoplasms                                     0
Conflict and Terrorism                        0
Diabetes Mellitus                             0
Chronic Kidney Disease                        0
Poisonings                                    0
Protein-Energy Malnutrition                   0
Road Injuries                                 0
Chronic Respiratory Diseases                  0
Cirrhosis and Other Chronic Liver Diseases    0
Digestive Diseases                            0
Fire, Heat, and Hot Substances                0
Acute Hepatitis                               0
dtype: int64
In [9]:
df.columns
Out[9]:
Index(['Country/Territory', 'Code', 'Year', 'Meningitis',
       'Alzheimer's Disease and Other Dementias', 'Parkinson's Disease',
       'Nutritional Deficiencies', 'Malaria', 'Drowning',
       'Interpersonal Violence', 'Maternal Disorders', 'HIV/AIDS',
       'Drug Use Disorders', 'Tuberculosis', 'Cardiovascular Diseases',
       'Lower Respiratory Infections', 'Neonatal Disorders',
       'Alcohol Use Disorders', 'Self-harm', 'Exposure to Forces of Nature',
       'Diarrheal Diseases', 'Environmental Heat and Cold Exposure',
       'Neoplasms', 'Conflict and Terrorism', 'Diabetes Mellitus',
       'Chronic Kidney Disease', 'Poisonings', 'Protein-Energy Malnutrition',
       'Road Injuries', 'Chronic Respiratory Diseases',
       'Cirrhosis and Other Chronic Liver Diseases', 'Digestive Diseases',
       'Fire, Heat, and Hot Substances', 'Acute Hepatitis'],
      dtype='object')
In [10]:
df.duplicated(keep='last')
Out[10]:
0       False
1       False
2       False
3       False
4       False
        ...  
6115    False
6116    False
6117    False
6118    False
6119    False
Length: 6120, dtype: bool
In [11]:
#great we don't have nulland duplicate values in our data set

Country/Territory¶

In [12]:
df['Country/Territory'].describe()
Out[12]:
count            6120
unique            204
top       Afghanistan
freq               30
Name: Country/Territory, dtype: object
In [13]:
# Country/Territory contain  nominal  data in text formate
In [14]:
#checking unique of variable
print(df['Country/Territory'].unique())
#counting the uniques
print(df['Country/Territory'].value_counts())

['Afghanistan' 'Albania' 'Algeria' 'American Samoa' 'Andorra' 'Angola'
 'Antigua and Barbuda' 'Argentina' 'Armenia' 'Australia' 'Austria'
 'Azerbaijan' 'Bahamas' 'Bahrain' 'Bangladesh' 'Barbados' 'Belarus'
 'Belgium' 'Belize' 'Benin' 'Bermuda' 'Bhutan' 'Bolivia'
 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Brunei' 'Bulgaria'
 'Burkina Faso' 'Burundi' 'Cambodia' 'Cameroon' 'Canada' 'Cape Verde'
 'Central African Republic' 'Chad' 'Chile' 'China' 'Colombia' 'Comoros'
 'Congo' 'Cook Islands' 'Costa Rica' "Cote d'Ivoire" 'Croatia' 'Cuba'
 'Cyprus' 'Czechia' 'Democratic Republic of Congo' 'Denmark' 'Djibouti'
 'Dominica' 'Dominican Republic' 'Ecuador' 'Egypt' 'El Salvador'
 'Equatorial Guinea' 'Eritrea' 'Estonia' 'Eswatini' 'Ethiopia' 'Fiji'
 'Finland' 'France' 'Gabon' 'Gambia' 'Georgia' 'Germany' 'Ghana' 'Greece'
 'Greenland' 'Grenada' 'Guam' 'Guatemala' 'Guinea' 'Guinea-Bissau'
 'Guyana' 'Haiti' 'Honduras' 'Hungary' 'Iceland' 'India' 'Indonesia'
 'Iran' 'Iraq' 'Ireland' 'Israel' 'Italy' 'Jamaica' 'Japan' 'Jordan'
 'Kazakhstan' 'Kenya' 'Kiribati' 'Kuwait' 'Kyrgyzstan' 'Laos' 'Latvia'
 'Lebanon' 'Lesotho' 'Liberia' 'Libya' 'Lithuania' 'Luxembourg'
 'Madagascar' 'Malawi' 'Malaysia' 'Maldives' 'Mali' 'Malta'
 'Marshall Islands' 'Mauritania' 'Mauritius' 'Mexico' 'Micronesia'
 'Moldova' 'Monaco' 'Mongolia' 'Montenegro' 'Morocco' 'Mozambique'
 'Myanmar' 'Namibia' 'Nauru' 'Nepal' 'Netherlands' 'New Zealand'
 'Nicaragua' 'Niger' 'Nigeria' 'Niue' 'North Korea' 'North Macedonia'
 'Northern Mariana Islands' 'Norway' 'Oman' 'Pakistan' 'Palau' 'Palestine'
 'Panama' 'Papua New Guinea' 'Paraguay' 'Peru' 'Philippines' 'Poland'
 'Portugal' 'Puerto Rico' 'Qatar' 'Romania' 'Russia' 'Rwanda'
 'Saint Kitts and Nevis' 'Saint Lucia' 'Saint Vincent and the Grenadines'
 'Samoa' 'San Marino' 'Sao Tome and Principe' 'Saudi Arabia' 'Senegal'
 'Serbia' 'Seychelles' 'Sierra Leone' 'Singapore' 'Slovakia' 'Slovenia'
 'Solomon Islands' 'Somalia' 'South Africa' 'South Korea' 'South Sudan'
 'Spain' 'Sri Lanka' 'Sudan' 'Suriname' 'Sweden' 'Switzerland' 'Syria'
 'Taiwan' 'Tajikistan' 'Tanzania' 'Thailand' 'Timor' 'Togo' 'Tokelau'
 'Tonga' 'Trinidad and Tobago' 'Tunisia' 'Turkey' 'Turkmenistan' 'Tuvalu'
 'Uganda' 'Ukraine' 'United Arab Emirates' 'United Kingdom'
 'United States' 'United States Virgin Islands' 'Uruguay' 'Uzbekistan'
 'Vanuatu' 'Venezuela' 'Vietnam' 'Yemen' 'Zambia' 'Zimbabwe']
Afghanistan         30
Papua New Guinea    30
Niue                30
North Korea         30
North Macedonia     30
                    ..
Greenland           30
Grenada             30
Guam                30
Guatemala           30
Zimbabwe            30
Name: Country/Territory, Length: 204, dtype: int64
In [ ]:

Year¶

In [15]:
df['Year'].describe()
Out[15]:
count    6120.000000
mean     2004.500000
std         8.656149
min      1990.000000
25%      1997.000000
50%      2004.500000
75%      2012.000000
max      2019.000000
Name: Year, dtype: float64
In [16]:
#checking unique of variable
print(df['Year'].unique())
#counting the uniques
print(df['Year'].value_counts())

[1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
 2018 2019]
1990    204
1991    204
2018    204
2017    204
2016    204
2015    204
2014    204
2013    204
2012    204
2011    204
2010    204
2009    204
2008    204
2007    204
2006    204
2005    204
2004    204
2003    204
2002    204
2001    204
2000    204
1999    204
1998    204
1997    204
1996    204
1995    204
1994    204
1993    204
1992    204
2019    204
Name: Year, dtype: int64
In [17]:
# In the year column contains ordinal data 
# it is equally distrubuted 
# we have data from year 1990 to 2019 = 30 year of death records we have

04. Meningitis - No. of People died from Meningitis¶

In [18]:
# No.of People died from Meningitis
In [19]:
df['Meningitis'].describe()
Out[19]:
count     6120.000000
mean      1719.701307
std       6672.006930
min          0.000000
25%         15.000000
50%        109.000000
75%        847.250000
max      98358.000000
Name: Meningitis, dtype: float64
In [20]:
sns.lineplot(data=df, x="Year", y="Meningitis")
Out[20]:
<AxesSubplot:xlabel='Year', ylabel='Meningitis'>
In [21]:
# No.of People died from Meningitis is following down trend pattern for overall population
In [22]:
sns.factorplot(x='Meningitis',y='Country/Territory',hue='Year',data=df,size=30,aspect =0.8, join= False)
Out[22]:
<seaborn.axisgrid.FacetGrid at 0x238f1a17040>
In [23]:
# Q. in which country maximum people were died due to Meningitis 
# A. india
# Q. in which year death percentage was high
# A. the year was 1990
In [24]:
diseases = [ 'Meningitis',
       "Alzheimer's Disease and Other Dementias", "Parkinson's Disease",
       'Nutritional Deficiencies', 'Malaria', 'Drowning',
       'Interpersonal Violence', 'Maternal Disorders', 'HIV/AIDS',
       'Drug Use Disorders', 'Tuberculosis', 'Cardiovascular Diseases',
       'Lower Respiratory Infections', 'Neonatal Disorders',
       'Alcohol Use Disorders', 'Self-harm', 'Exposure to Forces of Nature',
       'Diarrheal Diseases', 'Environmental Heat and Cold Exposure',
       'Neoplasms', 'Conflict and Terrorism', 'Diabetes Mellitus',
       'Chronic Kidney Disease', 'Poisonings', 'Protein-Energy Malnutrition',
       'Road Injuries', 'Chronic Respiratory Diseases',
       'Cirrhosis and Other Chronic Liver Diseases', 'Digestive Diseases',
       'Fire, Heat, and Hot Substances', 'Acute Hepatitis']
In [25]:
# Top 10 Country name No.of People died from Meningitis 
data = df.groupby(['Country/Territory'])["Meningitis"].sum().sort_values(ascending =False)[:10]
In [26]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,text_auto=True,title="No. of People died from Meningitis")

05. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias¶

In [27]:
## No.of People died from Alzheimer's Disease and Other Dementias
In [28]:
df["Alzheimer's Disease and Other Dementias"].describe()
Out[28]:
count      6120.000000
mean       4864.189379
std       18220.659072
min           0.000000
25%          90.000000
50%         666.500000
75%        2456.250000
max      320715.000000
Name: Alzheimer's Disease and Other Dementias, dtype: float64
In [29]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Alzheimer's Disease and Other Dementias")
Out[29]:
<AxesSubplot:xlabel='Year', ylabel="Alzheimer's Disease and Other Dementias">
In [30]:
# No.of People died from Alzheimer's Disease is following  up trend pattern for over all population death
In [31]:
sns.factorplot(x="Alzheimer's Disease and Other Dementias",y='Country/Territory',hue='Year',data=df,size=40,aspect =0.8, join= False)
Out[31]:
<seaborn.axisgrid.FacetGrid at 0x238f2abc3d0>
In [32]:
# Q. in which country maximum people were died due to Alzheimer's Disease 
# A. China
# Q. in which year death percentage was high
# A. the year was 1990
In [33]:
data = df.groupby(['Country/Territory'])["Alzheimer's Disease and Other Dementias"].sum().sort_values(ascending =False)[:10]
In [34]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,text_auto=True,title="No. of People died from Alzheimer's Disease and Other Dementias")

06. Parkinson's Disease - No. of People died from Parkinson's Disease¶

In [35]:
df["Parkinson's Disease"].describe()
Out[35]:
count     6120.000000
mean      1173.169118
std       4616.156238
min          0.000000
25%         27.000000
50%        164.000000
75%        609.250000
max      76990.000000
Name: Parkinson's Disease, dtype: float64
In [36]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Parkinson's Disease")
Out[36]:
<AxesSubplot:xlabel='Year', ylabel="Parkinson's Disease">
In [37]:
data = df.groupby(['Country/Territory'])["Parkinson's Disease"].sum().sort_values(ascending =False)[:10]
In [38]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Parkinson's Disease - No. of People died from Parkinson's Disease")

07 Nutritional Deficiencies - No. of People died from Nutritional Deficiencies¶

In [39]:
df["Nutritional Deficiencies"].describe()
Out[39]:
count      6120.000000
mean       2253.600000
std       10483.633601
min           0.000000
25%           9.000000
50%         119.000000
75%        1167.250000
max      268223.000000
Name: Nutritional Deficiencies, dtype: float64
In [40]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Nutritional Deficiencies")
Out[40]:
<AxesSubplot:xlabel='Year', ylabel='Nutritional Deficiencies'>
In [41]:
data = df.groupby(['Country/Territory'])["Nutritional Deficiencies"].sum().sort_values(ascending =False)[:10]
In [42]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="No. of People died from Nutritional Deficiencies")

08. Malaria - No. of People died from Malaria¶

In [43]:
df["Malaria"].describe()
Out[43]:
count      6120.000000
mean       4140.960131
std       18427.753137
min           0.000000
25%           0.000000
50%           0.000000
75%         393.000000
max      280604.000000
Name: Malaria, dtype: float64
In [44]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Malaria")
Out[44]:
<AxesSubplot:xlabel='Year', ylabel='Malaria'>
In [45]:
data = df.groupby(['Country/Territory'])["Malaria"].sum().sort_values(ascending =False)[:10]
In [46]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="No. of People died from Malaria")

09. Drowning - No. of People died from Drowning¶

In [47]:
df["Drowning"].describe()
Out[47]:
count      6120.000000
mean       1683.333170
std        8877.018366
min           0.000000
25%          34.000000
50%         177.000000
75%         698.000000
max      153773.000000
Name: Drowning, dtype: float64
In [48]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Drowning")
Out[48]:
<AxesSubplot:xlabel='Year', ylabel='Drowning'>
In [49]:
data = df.groupby(['Country/Territory'])["Drowning"].sum().sort_values(ascending =False)[:10]
In [50]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="No. of People died from Drowning")

10. Interpersonal Violence - No. of People died from Interpersonal Violence¶

In [51]:
df["Interpersonal Violence"].describe()
Out[51]:
count     6120.000000
mean      2083.797222
std       6917.006075
min          0.000000
25%         40.000000
50%        265.000000
75%        877.000000
max      69640.000000
Name: Interpersonal Violence, dtype: float64
In [52]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Interpersonal Violence")
Out[52]:
<AxesSubplot:xlabel='Year', ylabel='Interpersonal Violence'>
In [53]:
data = df.groupby(['Country/Territory'])["Interpersonal Violence"].sum().sort_values(ascending =False)[:10]
In [54]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="No. of People died from Interpersonal Violence")

11. Maternal Disorders - No. of People died from Maternal Disorders¶

In [55]:
df["Maternal Disorders"].describe()
Out[55]:
count      6120.000000
mean       1262.589216
std        6057.973183
min           0.000000
25%           5.000000
50%          54.000000
75%         734.000000
max      107929.000000
Name: Maternal Disorders, dtype: float64
In [56]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Maternal Disorders")
Out[56]:
<AxesSubplot:xlabel='Year', ylabel='Maternal Disorders'>
In [57]:
data = df.groupby(['Country/Territory'])["Maternal Disorders"].sum().sort_values(ascending =False)[:10]
In [58]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Maternal Disorders - No. of People died from Maternal Disorders")

12. Drug Use Disorders - No. of People died from Drug Use Disorders¶

In [59]:
df["Drug Use Disorders"].describe()
Out[59]:
count     6120.000000
mean       434.006699
std       2898.761628
min          0.000000
25%          3.000000
50%         20.000000
75%        129.000000
max      65717.000000
Name: Drug Use Disorders, dtype: float64
In [60]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Drug Use Disorders")
Out[60]:
<AxesSubplot:xlabel='Year', ylabel='Drug Use Disorders'>
In [61]:
data = df.groupby(['Country/Territory'])["Drug Use Disorders"].sum().sort_values(ascending =False)[:10]
In [62]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Drug Use Disorders - No. of People died from Drug Use Disorders")

13. Tuberculosis - No. of People died from Tuberculosis¶

In [63]:
df["Tuberculosis"].describe()
Out[63]:
count      6120.000000
mean       7491.928595
std       39549.977578
min           0.000000
25%          35.000000
50%         417.000000
75%        2924.250000
max      657515.000000
Name: Tuberculosis, dtype: float64
In [64]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Tuberculosis")
Out[64]:
<AxesSubplot:xlabel='Year', ylabel='Tuberculosis'>
In [65]:
data = df.groupby(['Country/Territory'])["Tuberculosis"].sum().sort_values(ascending =False)[:10]
In [66]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Tuberculosis - No. of People died from Tuberculosis")

14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases¶

In [67]:
df["Cardiovascular Diseases"].describe()
Out[67]:
count    6.120000e+03
mean     7.316045e+04
std      2.915775e+05
min      4.000000e+00
25%      2.028000e+03
50%      1.174200e+04
75%      4.254650e+04
max      4.584273e+06
Name: Cardiovascular Diseases, dtype: float64
In [68]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Cardiovascular Diseases")
Out[68]:
<AxesSubplot:xlabel='Year', ylabel='Cardiovascular Diseases'>
In [69]:
data = df.groupby(['Country/Territory'])["Cardiovascular Diseases"].sum().sort_values(ascending =False)[:10]
In [70]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Cardiovascular Diseases - No. of People died from Cardiovascular Diseases")

15. Lower Respiratory Infections - No. of People died from Lower Respiratory¶

In [71]:
df["Lower Respiratory Infections"].describe()
Out[71]:
count      6120.000000
mean      13687.914706
std       48031.720009
min           0.000000
25%         345.000000
50%        2126.500000
75%       10161.250000
max      690913.000000
Name: Lower Respiratory Infections, dtype: float64
In [72]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Lower Respiratory Infections")
Out[72]:
<AxesSubplot:xlabel='Year', ylabel='Lower Respiratory Infections'>
In [73]:
data = df.groupby(['Country/Territory'])["Lower Respiratory Infections"].sum().sort_values(ascending =False)[:10]
In [74]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Lower Respiratory Infections - No. of People died from Lower Respiratory")

16. Neonatal Disorders - No. of People died from Neonatal Disorders¶

In [75]:
df["Neonatal Disorders"].describe()
Out[75]:
count      6120.000000
mean      12558.942647
std       56058.366412
min           0.000000
25%         131.000000
50%         916.000000
75%        7419.750000
max      852761.000000
Name: Neonatal Disorders, dtype: float64
In [76]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Neonatal Disorders")
Out[76]:
<AxesSubplot:xlabel='Year', ylabel='Neonatal Disorders'>
In [77]:
data = df.groupby(['Country/Territory'])["Neonatal Disorders"].sum().sort_values(ascending =False)[:10]
In [78]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title=" Neonatal Disorders - No. of People died from Neonatal Disorders")

17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders¶

In [79]:
df["Alcohol Use Disorders"].describe()
Out[79]:
count     6120.000000
mean       787.421242
std       3545.823616
min          0.000000
25%          9.000000
50%         80.000000
75%        316.000000
max      55200.000000
Name: Alcohol Use Disorders, dtype: float64
In [80]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Alcohol Use Disorders")
Out[80]:
<AxesSubplot:xlabel='Year', ylabel='Alcohol Use Disorders'>
In [81]:
data = df.groupby(['Country/Territory'])["Alcohol Use Disorders"].sum().sort_values(ascending =False)[:10]
In [82]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title=" Alcohol Use Disorders - No. of People died from Alcohol Use Disorders")

18. Self-harm - No. of People died from Self-harm¶

In [83]:
df["Self-harm"].describe()
Out[83]:
count      6120.000000
mean       3874.825327
std       18425.616418
min           0.000000
25%          94.000000
50%         533.000000
75%        1882.250000
max      220357.000000
Name: Self-harm, dtype: float64
In [84]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Self-harm")
Out[84]:
<AxesSubplot:xlabel='Year', ylabel='Self-harm'>
In [85]:
data = df.groupby(['Country/Territory'])["Self-harm"].sum().sort_values(ascending =False)[:10]
In [86]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title=" Self-harm - No. of People died from Self-harm")

19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature¶

In [87]:
df["Exposure to Forces of Nature"].describe()
Out[87]:
count      6120.000000
mean        243.485621
std        4717.104377
min           0.000000
25%           0.000000
50%           0.000000
75%          12.000000
max      222641.000000
Name: Exposure to Forces of Nature, dtype: float64
In [88]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Exposure to Forces of Nature")
Out[88]:
<AxesSubplot:xlabel='Year', ylabel='Exposure to Forces of Nature'>
In [89]:
data = df.groupby(['Country/Territory'])["Exposure to Forces of Nature"].sum().sort_values(ascending =False)[:10]
In [90]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title=" Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature")

20. Diarrheal Diseases - No. of People died from Diarrheal Diseases¶

In [91]:
df["Diarrheal Diseases"].describe()
Out[91]:
count    6.120000e+03
mean     1.082280e+04
std      6.541617e+04
min      0.000000e+00
25%      2.000000e+01
50%      2.965000e+02
75%      3.946750e+03
max      1.119477e+06
Name: Diarrheal Diseases, dtype: float64
In [92]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Diarrheal Diseases")
Out[92]:
<AxesSubplot:xlabel='Year', ylabel='Diarrheal Diseases'>
In [93]:
data = df.groupby(['Country/Territory'])["Diarrheal Diseases"].sum().sort_values(ascending =False)[:10]
In [94]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Diarrheal Diseases - No. of People died from Diarrheal Diseases")

21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure¶

In [95]:
df["Environmental Heat and Cold Exposure"].describe()
Out[95]:
count     6120.000000
mean       292.295915
std       1704.466356
min          0.000000
25%          2.000000
50%         21.000000
75%        109.000000
max      29048.000000
Name: Environmental Heat and Cold Exposure, dtype: float64
In [96]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Environmental Heat and Cold Exposure")
Out[96]:
<AxesSubplot:xlabel='Year', ylabel='Environmental Heat and Cold Exposure'>
In [97]:
data = df.groupby(['Country/Territory'])["Environmental Heat and Cold Exposure"].sum().sort_values(ascending =False)[:10]
In [98]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure")

22. Neoplasms - No. of People died from Neoplasms¶

In [99]:
df["Neoplasms"].describe()
Out[99]:
count    6.120000e+03
mean     3.754224e+04
std      1.615584e+05
min      1.000000e+00
25%      8.097500e+02
50%      5.629500e+03
75%      2.014775e+04
max      2.716551e+06
Name: Neoplasms, dtype: float64
In [100]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Neoplasms")
Out[100]:
<AxesSubplot:xlabel='Year', ylabel='Neoplasms'>
In [101]:
data = df.groupby(['Country/Territory'])["Neoplasms"].sum().sort_values(ascending =False)[:10]
In [102]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Neoplasms - No. of People died from Neoplasms")

23. Conflict and Terrorism - No. of People died from Conflict and Terrorism¶

In [103]:
df["Conflict and Terrorism"].describe()
Out[103]:
count      6120.000000
mean        538.243954
std        7033.308187
min           0.000000
25%           0.000000
50%           0.000000
75%          23.000000
max      503532.000000
Name: Conflict and Terrorism, dtype: float64
In [104]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Conflict and Terrorism")
Out[104]:
<AxesSubplot:xlabel='Year', ylabel='Conflict and Terrorism'>
In [105]:
data = df.groupby(['Country/Territory'])["Conflict and Terrorism"].sum().sort_values(ascending =False)[:10]
In [106]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Conflict and Terrorism - No. of People died from Conflict and Terrorism")

24. Diabetes Mellitus - No. of People died from Diabetes Mellitus¶

In [107]:
df["Diabetes Mellitus"].describe()
Out[107]:
count      6120.000000
mean       5138.704575
std       16773.081040
min           1.000000
25%         236.000000
50%        1087.000000
75%        2954.000000
max      273089.000000
Name: Diabetes Mellitus, dtype: float64
In [108]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Diabetes Mellitus")
Out[108]:
<AxesSubplot:xlabel='Year', ylabel='Diabetes Mellitus'>
In [109]:
data = df.groupby(['Country/Territory'])["Diabetes Mellitus"].sum().sort_values(ascending =False)[:10]
In [110]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Diabetes Mellitus - No. of People died from Diabetes Mellitus")

25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease¶

In [111]:
df["Chronic Kidney Disease"].describe()
Out[111]:
count      6120.000000
mean       4724.132680
std       16470.429969
min           0.000000
25%         145.750000
50%         822.000000
75%        2922.500000
max      222922.000000
Name: Chronic Kidney Disease, dtype: float64
In [112]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Chronic Kidney Disease")
Out[112]:
<AxesSubplot:xlabel='Year', ylabel='Chronic Kidney Disease'>
In [113]:
data = df.groupby(['Country/Territory'])["Chronic Kidney Disease"].sum().sort_values(ascending =False)[:10]
In [114]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Chronic Kidney Disease - No. of People died from Chronic Kidney Disease")

26. Poisonings - No. of People died from Poisoning¶

In [115]:
df["Poisonings"].describe()
Out[115]:
count     6120.000000
mean       425.013399
std       2022.640521
min          0.000000
25%          6.000000
50%         52.500000
75%        254.000000
max      30883.000000
Name: Poisonings, dtype: float64
In [116]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Poisonings")
Out[116]:
<AxesSubplot:xlabel='Year', ylabel='Poisonings'>
In [117]:
data = df.groupby(['Country/Territory'])["Poisonings"].sum().sort_values(ascending =False)[:10]
In [118]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Poisonings - No. of People died from Poisoning")

27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition¶

In [119]:
df["Protein-Energy Malnutrition"].describe()
Out[119]:
count      6120.000000
mean       1965.994281
std        8255.999063
min           0.000000
25%           5.000000
50%          92.000000
75%        1042.500000
max      202241.000000
Name: Protein-Energy Malnutrition, dtype: float64
In [120]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Protein-Energy Malnutrition")
Out[120]:
<AxesSubplot:xlabel='Year', ylabel='Protein-Energy Malnutrition'>
In [121]:
data = df.groupby(['Country/Territory'])["Protein-Energy Malnutrition"].sum().sort_values(ascending =False)[:10]
In [122]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition")

28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases¶

In [123]:
df["Chronic Respiratory Diseases"].describe()
Out[123]:
count    6.120000e+03
mean     1.709237e+04
std      1.051572e+05
min      1.000000e+00
25%      2.890000e+02
50%      1.689000e+03
75%      5.249750e+03
max      1.366039e+06
Name: Chronic Respiratory Diseases, dtype: float64
In [124]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Chronic Respiratory Diseases")
Out[124]:
<AxesSubplot:xlabel='Year', ylabel='Chronic Respiratory Diseases'>
In [125]:
data = df.groupby(['Country/Territory'])["Chronic Respiratory Diseases"].sum().sort_values(ascending =False)[:10]
In [126]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases")

29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases¶

In [127]:
df["Cirrhosis and Other Chronic Liver Diseases"].describe()
Out[127]:
count      6120.000000
mean       6124.072059
std       20688.118580
min           0.000000
25%         154.000000
50%        1210.000000
75%        3547.250000
max      270037.000000
Name: Cirrhosis and Other Chronic Liver Diseases, dtype: float64
In [128]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Cirrhosis and Other Chronic Liver Diseases")
Out[128]:
<AxesSubplot:xlabel='Year', ylabel='Cirrhosis and Other Chronic Liver Diseases'>
In [129]:
data = df.groupby(['Country/Territory'])["Cirrhosis and Other Chronic Liver Diseases"].sum().sort_values(ascending =False)[:10]
In [130]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases")

30. Digestive Diseases - No. of People died from Digestive Diseases¶

In [131]:
df["Digestive Diseases"].describe()
Out[131]:
count      6120.000000
mean      10725.267157
std       37228.051096
min           0.000000
25%         284.000000
50%        2185.000000
75%        6080.000000
max      464914.000000
Name: Digestive Diseases, dtype: float64
In [132]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Digestive Diseases")
Out[132]:
<AxesSubplot:xlabel='Year', ylabel='Digestive Diseases'>
In [133]:
data = df.groupby(['Country/Territory'])["Digestive Diseases"].sum().sort_values(ascending =False)[:10]
In [134]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Digestive Diseases - No. of People died from Digestive Diseases")

31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances¶

In [135]:
df["Fire, Heat, and Hot Substances"].describe()
Out[135]:
count     6120.000000
mean       588.711438
std       2128.595120
min          0.000000
25%         17.000000
50%        126.000000
75%        450.000000
max      25876.000000
Name: Fire, Heat, and Hot Substances, dtype: float64
In [136]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Fire, Heat, and Hot Substances")
Out[136]:
<AxesSubplot:xlabel='Year', ylabel='Fire, Heat, and Hot Substances'>
In [137]:
data = df.groupby(['Country/Territory'])["Fire, Heat, and Hot Substances"].sum().sort_values(ascending =False)[:10]
In [138]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances")

32. Acute Hepatitis - No. of People died from Acute Hepatitis¶

In [139]:
df["Acute Hepatitis"].describe()
Out[139]:
count     6120.000000
mean       618.429902
std       4186.023497
min          0.000000
25%          2.000000
50%         15.000000
75%        160.000000
max      64305.000000
Name: Acute Hepatitis, dtype: float64
In [140]:
plt.figure(figsize=(10,4))
sns.lineplot(data=df, x="Year", y="Acute Hepatitis")
Out[140]:
<AxesSubplot:xlabel='Year', ylabel='Acute Hepatitis'>
In [141]:
data = df.groupby(['Country/Territory'])["Acute Hepatitis"].sum().sort_values(ascending =False)[:10]
In [142]:
px.bar(data,x = data.index , y = data.values,text=data.index,color = data.index,title="Acute Hepatitis - No. of People died from Acute Hepatitis")

Severity Of disease in the countries¶

In [143]:
df1 = df.drop('Year',axis=1).groupby('Code').sum().reset_index()
In [144]:
for x in df1.index:
    y=df1.Code.iloc[x]
    temp=df1.set_index('Code').iloc[0].nlargest(5)
    plt.figure(figsize=(12,6))
    plt.bar(data=temp ,x = temp.index , height = temp.values, width=0.9, color = ['red','magenta','blue','gold','green',  ])
    plt.xticks(rotation='vertical')
    plt.xlabel("DISEASES" , size = 10)
    plt.ylabel('TOTAL DEATHS IN LAST 30 YEARS',size = 10)
    plt.title(y.upper() +' Severity',size =10)

" Top 10 Countries with the highest death rates worldwide"¶

In [145]:
df1['Total_Deaths']= df1.sum(axis=1)
In [146]:
sumall = df1[['Code','Total_Deaths']].sort_values('Total_Deaths',ascending =False)[:10]
In [147]:
px.bar(sumall,x = 'Code' , y = "Total_Deaths",text="Code",color = "Code",title="Countries with the highest death rates worldwide")
In [148]:
disease = df1.sum()[1:-1].to_frame().reset_index()
In [149]:
disease.rename(mapper={'index':'Disease',0:'Total_Deaths'},axis=1,inplace=True)
In [150]:
disease = disease.sort_values(by='Total_Deaths',ascending=False).reset_index(drop=True)
In [151]:
# Aggregating Countires with values lesser than 23713931 into Others
disease.loc[disease.Total_Deaths <23713931 ].sum()
disease.loc[len(disease.index)] = ['Others',98347130]
disease.drop(disease.index[16:-1],axis=0,inplace =True)
disease.reset_index(drop=True,inplace=True)
In [152]:
fig = px.pie(disease, names = 'Disease' , values = 'Total_Deaths', color_discrete_sequence=px.colors.sequential.Plasma_r,title="Highest death rates" )
fig.update_traces(textposition='inside', textinfo='percent+label',)
fig.update_layout(margin=dict(t=0, b=0, l=0, r=0))
fig.update(layout_showlegend=False)

Conclusions:-¶

CHINA , INDIA & USA has highest death rates due to¶

Cardiovascular,, Neoplasms ,and Respiratory are the top 3 killer diseases in the world.¶

In [ ]: